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Which two parameter expressions should you use?
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values:
• learning_rate: any value between 0.001 and 0.1
• batch_size: 16, 32, or 64
You need to configure the search space for the Hyperdrive experiment.
Which two parameter expressions should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A . a choice expression for learning_rate
B . a uniform expression for learning_rate
C . a normal expression for batch_size
D . a choice expression for batch_size
E . a uniform expression for batch_size
Answer: B,D
Explanation:
B: Continuous hyperparameters are specified as a distribution over a continuous range of values. Supported distributions include:
✑ uniform(low, high) – Returns a value uniformly distributed between low and high
D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
✑ one or more comma-separated values
✑ a range object
✑ any arbitrary list object
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
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